Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Yoruba
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
File size: 6,270 Bytes
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Introduction to the Yoruba GV NER dataset: A Yoruba Global Voices (News) Named Entity Recognition Dataset"""
import datasets
logger = datasets.logging.get_logger(__name__)
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{alabi-etal-2020-massive,
title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Yorùbá} and {T}wi",
author = "Alabi, Jesujoba and
Amponsah-Kaakyire, Kwabena and
Adelani, David and
Espa{\\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
pages = "2754--2762",
language = "English",
ISBN = "979-10-95546-34-4",
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
The Yoruba GV NER dataset is a labeled dataset for named entity recognition in Yoruba. The texts were obtained from
Yoruba Global Voices News articles https://yo.globalvoices.org/ . We concentrate on
four types of named entities: persons [PER], locations [LOC], organizations [ORG], and dates & time [DATE].
The Yoruba GV NER data files contain 2 columns separated by a tab ('\t'). Each word has been put on a separate line and
there is an empty line after each sentences i.e the CoNLL format. The first item on each line is a word, the second
is the named entity tag. The named entity tags have the format I-TYPE which means that the word is inside a phrase
of type TYPE. For every multi-word expression like 'New York', the first word gets a tag B-TYPE and the subsequent words
have tags I-TYPE, a word with tag O is not part of a phrase. The dataset is in the BIO tagging scheme.
For more details, see https://www.aclweb.org/anthology/2020.lrec-1.335/
"""
_URL = "https://github.com/ajesujoba/YorubaTwi-Embedding/raw/master/Yoruba/Yoruba-NER/"
_TRAINING_FILE = "train.tsv"
_DEV_FILE = "valid.tsv"
_TEST_FILE = "test.tsv"
class YorubaGvNerConfig(datasets.BuilderConfig):
"""BuilderConfig for YorubaGvNer"""
def __init__(self, **kwargs):
"""BuilderConfig for YorubaGvNer.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(YorubaGvNerConfig, self).__init__(**kwargs)
class YorubaGvNer(datasets.GeneratorBasedBuilder):
"""Yoruba GV NER dataset."""
BUILDER_CONFIGS = [
YorubaGvNerConfig(
name="yoruba_gv_ner", version=datasets.Version("1.0.0"), description="Yoruba GV NER dataset"
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
"B-DATE",
"I-DATE",
]
)
),
}
),
supervised_keys=None,
homepage="https://www.aclweb.org/anthology/2020.lrec-1.335/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
"dev": f"{_URL}{_DEV_FILE}",
"test": f"{_URL}{_TEST_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
ner_tags = []
for line in f:
line = line.strip()
if line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
ner_tags = []
else:
# yoruba_gv_ner tokens are tab separated
splits = line.strip().split("\t")
tokens.append(splits[0])
ner_tags.append(splits[1].rstrip())
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
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